Indian Permanent Account Numbers¶

Introduction¶

The function clean_in_pan() cleans a column containing Indian Permanent Account number (PAN) strings, and standardizes them in a given format. The function validate_in_pan() validates either a single PAN strings, a column of PAN strings or a DataFrame of PAN strings, returning True if the value is valid, and False otherwise.

PAN strings can be converted to the following formats via the output_format parameter:

compact: only number strings without any seperators or whitespace, like “ACUPA7085R”
standard: PAN strings with proper whitespace in the proper places. Note that in the case of PAN, the compact format is the same as the standard one.
info: return a dictionary containing information that can be decoded from the PAN, like {‘card_holder_type’: ‘Individual’, ‘initial’: ‘A’}.
mask: mask the PAN as per CBDT masking standard, like “ACUPAXXXXR”.

Invalid parsing is handled with the errors parameter:

coerce (default): invalid parsing will be set to NaN
ignore: invalid parsing will return the input
raise: invalid parsing will raise an exception

The following sections demonstrate the functionality of clean_in_pan() and validate_in_pan().

An example dataset containing PAN strings¶

[1]:

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "pan": [
            'ACUPA7085R',
            '234123412347',
            '7542011030',
            '7552A10004',
            '8019010008',
            "hello",
            np.nan,
            "NULL",
        ],
        "address": [
            "123 Pine Ave.",
            "main st",
            "1234 west main heights 57033",
            "apt 1 789 s maple rd manhattan",
            "robie house, 789 north main street",
            "1111 S Figueroa St, Los Angeles, CA 90015",
            "(staples center) 1111 S Figueroa St, Los Angeles",
            "hello",
        ]
    }
)
df

[1]:

	pan	address
0	ACUPA7085R	123 Pine Ave.
1	234123412347	main st
2	7542011030	1234 west main heights 57033
3	7552A10004	apt 1 789 s maple rd manhattan
4	8019010008	robie house, 789 north main street
5	hello	1111 S Figueroa St, Los Angeles, CA 90015
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles
7	NULL	hello

1. Default `clean_in_pan`¶

By default, clean_in_pan will clean pan strings and output them in the standard format with proper separators.

[2]:

from dataprep.clean import clean_in_pan
clean_in_pan(df, column = "pan")

[2]:

	pan	address	pan_clean
0	ACUPA7085R	123 Pine Ave.	ACUPA7085R
1	234123412347	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	robie house, 789 north main street	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

2. Output formats¶

This section demonstrates the output parameter.

`standard` (default)¶

[3]:

clean_in_pan(df, column = "pan", output_format="standard")

[3]:

	pan	address	pan_clean
0	ACUPA7085R	123 Pine Ave.	ACUPA7085R
1	234123412347	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	robie house, 789 north main street	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

`compact`¶

[4]:

clean_in_pan(df, column = "pan", output_format="compact")

[4]:

	pan	address	pan_clean
0	ACUPA7085R	123 Pine Ave.	ACUPA7085R
1	234123412347	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	robie house, 789 north main street	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

`info`¶

[5]:

clean_in_pan(df, column = "pan", output_format="info")

[5]:

	pan	address	pan_clean
0	ACUPA7085R	123 Pine Ave.	{'card_holder_type': 'Individual', 'initial': ...
1	234123412347	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	robie house, 789 north main street	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

`mask`¶

[6]:

clean_in_pan(df, column = "pan", output_format="mask")

[6]:

	pan	address	pan_clean
0	ACUPA7085R	123 Pine Ave.	ACUPAXXXXR
1	234123412347	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	robie house, 789 north main street	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

3. `inplace` parameter¶

This deletes the given column from the returned DataFrame. A new column containing cleaned PAN strings is added with a title in the format "{original title}_clean".

[7]:

clean_in_pan(df, column="pan", inplace=True)

[7]:

	pan_clean	address
0	ACUPA7085R	123 Pine Ave.
1	NaN	main st
2	NaN	1234 west main heights 57033
3	NaN	apt 1 789 s maple rd manhattan
4	NaN	robie house, 789 north main street
5	NaN	1111 S Figueroa St, Los Angeles, CA 90015
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles
7	NaN	hello

4. `errors` parameter¶

`coerce` (default)¶

[8]:

clean_in_pan(df, "pan", errors="coerce")

[8]:

	pan	address	pan_clean
0	ACUPA7085R	123 Pine Ave.	ACUPA7085R
1	234123412347	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	robie house, 789 north main street	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

`ignore`¶

[9]:

clean_in_pan(df, "pan", errors="ignore")

[9]:

	pan	address	pan_clean
0	ACUPA7085R	123 Pine Ave.	ACUPA7085R
1	234123412347	main st	234123412347
2	7542011030	1234 west main heights 57033	7542011030
3	7552A10004	apt 1 789 s maple rd manhattan	7552A10004
4	8019010008	robie house, 789 north main street	8019010008
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	hello
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

4. `validate_in_pan()`¶

validate_in_pan() returns True when the input is a valid PAN. Otherwise it returns False.

The input of validate_in_pan() can be a string, a Pandas DataSeries, a Dask DataSeries, a Pandas DataFrame and a dask DataFrame.

When the input is a string, a Pandas DataSeries or a Dask DataSeries, user doesn’t need to specify a column name to be validated.

When the input is a Pandas DataFrame or a dask DataFrame, user can both specify or not specify a column name to be validated. If user specify the column name, validate_in_pan() only returns the validation result for the specified column. If user doesn’t specify the column name, validate_in_pan() returns the validation result for the whole DataFrame.

[10]:

from dataprep.clean import validate_in_pan
print(validate_in_pan('ACUPA7085R'))
print(validate_in_pan('234123412347'))
print(validate_in_pan('7542011030'))
print(validate_in_pan('7552A10004'))
print(validate_in_pan('8019010008'))
print(validate_in_pan("hello"))
print(validate_in_pan(np.nan))
print(validate_in_pan("NULL"))

True
False
False
False
False
False
False
False

Series¶

[11]:

validate_in_pan(df["pan"])

[11]:

0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
Name: pan, dtype: bool

DataFrame + Specify Column¶

[12]:

validate_in_pan(df, column="pan")

[12]:

0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
Name: pan, dtype: bool

Only DataFrame¶

[13]:

validate_in_pan(df)

[13]:

	pan	address
0	True	False
1	False	False
2	False	False
3	False	False
4	False	False
5	False	False
6	False	False
7	False	False

[ ]:

Indian Permanent Account Numbers¶

Introduction¶

An example dataset containing PAN strings¶

1. Default clean_in_pan¶

2. Output formats¶

standard (default)¶

compact¶

info¶

mask¶

3. inplace parameter¶

4. errors parameter¶

coerce (default)¶

ignore¶

4. validate_in_pan()¶